diff --git a/src/cellmap_data/dataset_writer.py b/src/cellmap_data/dataset_writer.py
index 6622eb7..24f8c75 100644
--- a/src/cellmap_data/dataset_writer.py
+++ b/src/cellmap_data/dataset_writer.py
@@ -14,6 +14,9 @@
 logger = logging.getLogger(__name__)
 logger.setLevel(logging.INFO)
 
+# Special keys that should not be written to disk
+_METADATA_KEYS = {"idx"}
+
 
 # %%
 class CellMapDatasetWriter(Dataset):
@@ -352,13 +355,34 @@ def __setitem__(
         if isinstance(idx, (torch.Tensor, np.ndarray, Sequence)):
             if isinstance(idx, torch.Tensor):
                 idx = idx.cpu().numpy()
-            for i in idx:
-                self.__setitem__(i, arrays)
+            for batch_idx, i in enumerate(idx):
+                # Extract the data for this specific item in the batch
+                item_arrays = {}
+                for array_name, array in arrays.items():
+                    # Skip special metadata keys
+                    if array_name in _METADATA_KEYS:
+                        continue
+                    if isinstance(array, (int, float)):
+                        # Scalar values are the same for all items
+                        item_arrays[array_name] = array
+                    elif isinstance(array, dict):
+                        # Dictionary of arrays - extract batch item from each
+                        item_arrays[array_name] = {
+                            label: label_array[batch_idx]
+                            for label, label_array in array.items()
+                        }
+                    else:
+                        # Regular array - extract the batch item
+                        item_arrays[array_name] = array[batch_idx]
+                self.__setitem__(i, item_arrays)
             return
 
         self._current_idx = idx
         self._current_center = self.get_center(self._current_idx)
         for array_name, array in arrays.items():
+            # Skip special metadata keys
+            if array_name in _METADATA_KEYS:
+                continue
             if isinstance(array, (int, float)):
                 for label in self.classes:
                     self.target_array_writers[array_name][label][
@@ -373,7 +397,7 @@ def __setitem__(
                 for c, label in enumerate(self.classes):
                     self.target_array_writers[array_name][label][
                         self._current_center
-                    ] = array[:, c, ...]
+                    ] = array[c, ...]
 
     def __repr__(self) -> str:
         """Returns a string representation of the dataset."""
diff --git a/src/cellmap_data/subdataset.py b/src/cellmap_data/subdataset.py
index dba384d..c2eaf80 100644
--- a/src/cellmap_data/subdataset.py
+++ b/src/cellmap_data/subdataset.py
@@ -4,9 +4,9 @@
 import torch
 from torch.utils.data import Subset
 
-from .dataset_writer import CellMapDatasetWriter
 from .base_dataset import CellMapBaseDataset
 from .dataset import CellMapDataset
+from .dataset_writer import CellMapDatasetWriter
 from .multidataset import CellMapMultiDataset
 from .mutable_sampler import MutableSubsetRandomSampler
 from .utils.sampling import min_redundant_inds
diff --git a/tests/test_dataset_writer_batch.py b/tests/test_dataset_writer_batch.py
new file mode 100644
index 0000000..fc36e44
--- /dev/null
+++ b/tests/test_dataset_writer_batch.py
@@ -0,0 +1,209 @@
+"""
+Tests for CellMapDatasetWriter batch operations.
+
+Tests that the writer correctly handles batched write operations.
+"""
+
+import numpy as np
+import pytest
+import torch
+
+from cellmap_data import CellMapDatasetWriter
+
+from .test_helpers import create_test_dataset
+
+
+class TestDatasetWriterBatchOperations:
+    """Test suite for batch write operations in DatasetWriter."""
+
+    @pytest.fixture
+    def writer_setup(self, tmp_path):
+        """Create writer and config for batch write tests.
+
+        Returns a tuple of (writer, config) where writer is a CellMapDatasetWriter
+        configured for testing batch operations.
+        """
+        # Create input data
+        config = create_test_dataset(
+            tmp_path / "input",
+            raw_shape=(64, 64, 64),
+            num_classes=2,
+            raw_scale=(8.0, 8.0, 8.0),
+        )
+
+        # Output path
+        output_path = tmp_path / "output" / "predictions.zarr"
+
+        target_bounds = {
+            "pred": {
+                "x": [0, 512],
+                "y": [0, 512],
+                "z": [0, 512],
+            }
+        }
+
+        writer = CellMapDatasetWriter(
+            raw_path=config["raw_path"],
+            target_path=str(output_path),
+            classes=["class_0", "class_1"],
+            input_arrays={"raw": {"shape": (32, 32, 32), "scale": (8.0, 8.0, 8.0)}},
+            target_arrays={"pred": {"shape": (32, 32, 32), "scale": (8.0, 8.0, 8.0)}},
+            target_bounds=target_bounds,
+        )
+
+        return writer, config
+
+    def test_batch_write_with_tensor_indices(self, writer_setup):
+        """Test writing with a batch of tensor indices."""
+        writer, config = writer_setup
+
+        # Simulate batch predictions
+        batch_size = 8
+        indices = torch.tensor(list(range(batch_size)))
+
+        # Create predictions with shape (batch_size, num_classes, *spatial_dims)
+        predictions = torch.randn(batch_size, 2, 32, 32, 32)
+
+        # This should not raise an error
+        writer[indices] = {"pred": predictions}
+
+    def test_batch_write_with_numpy_indices(self, writer_setup):
+        """Test writing with a batch of numpy indices."""
+        writer, config = writer_setup
+
+        # Simulate batch predictions
+        batch_size = 4
+        indices = np.array(list(range(batch_size)))
+
+        # Create predictions
+        predictions = np.random.randn(batch_size, 2, 32, 32, 32).astype(np.float32)
+
+        # This should not raise an error
+        writer[indices] = {"pred": predictions}
+
+    def test_batch_write_with_list_indices(self, writer_setup):
+        """Test writing with a batch of list indices."""
+        writer, config = writer_setup
+
+        # Simulate batch predictions
+        batch_size = 4
+        indices = [0, 1, 2, 3]
+
+        # Create predictions
+        predictions = torch.randn(batch_size, 2, 32, 32, 32)
+
+        # This should not raise an error
+        writer[indices] = {"pred": predictions}
+
+    def test_batch_write_large_batch(self, writer_setup):
+        """Test writing with a large batch size (as in the error case)."""
+        writer, config = writer_setup
+
+        # Simulate the error case: batch_size=32
+        batch_size = 32
+        indices = torch.tensor(list(range(batch_size)))
+
+        # Create predictions with shape (32, 2, 32, 32, 32)
+        predictions = torch.randn(batch_size, 2, 32, 32, 32)
+
+        # This should not raise ValueError about shape mismatch
+        writer[indices] = {"pred": predictions}
+
+    def test_batch_write_with_dict_arrays(self, writer_setup):
+        """Test writing with dictionary of arrays per class."""
+        writer, config = writer_setup
+
+        batch_size = 4
+        indices = torch.tensor(list(range(batch_size)))
+
+        # Create predictions as dictionary
+        predictions_dict = {
+            "class_0": torch.randn(batch_size, 32, 32, 32),
+            "class_1": torch.randn(batch_size, 32, 32, 32),
+        }
+
+        # This should not raise an error
+        writer[indices] = {"pred": predictions_dict}
+
+    def test_batch_write_2d_data(self, tmp_path):
+        """Test batch writing for 2D data (3D with singleton z dimension)."""
+        # Import kept at module level; reuse create_test_dataset here
+
+        # Create test dataset with thin Z dimension to simulate 2D
+        config = create_test_dataset(
+            tmp_path / "input",
+            raw_shape=(1, 128, 128),  # Thin z dimension
+            num_classes=1,
+            raw_scale=(8.0, 4.0, 4.0),
+        )
+
+        output_path = tmp_path / "output_2d.zarr"
+
+        target_bounds = {
+            "pred": {
+                "z": [0, 8],
+                "y": [0, 512],
+                "x": [0, 512],
+            }
+        }
+
+        writer = CellMapDatasetWriter(
+            raw_path=config["raw_path"],
+            target_path=str(output_path),
+            classes=["class_0"],
+            input_arrays={"raw": {"shape": (1, 64, 64), "scale": (8.0, 4.0, 4.0)}},
+            target_arrays={"pred": {"shape": (1, 64, 64), "scale": (8.0, 4.0, 4.0)}},
+            axis_order="zyx",
+            target_bounds=target_bounds,
+        )
+
+        # Test batch write with thin-z 3D data
+        batch_size = 4
+        indices = torch.tensor(list(range(batch_size)))
+        predictions = torch.randn(batch_size, 1, 1, 64, 64)
+
+        # This should not raise an error
+        writer[indices] = {"pred": predictions}
+
+    def test_single_item_write_still_works(self, writer_setup):
+        """Test that single item writes still work correctly."""
+        writer, config = writer_setup
+
+        # Single item write
+        idx = 0
+        predictions = torch.randn(2, 32, 32, 32)
+
+        # This should work as before
+        writer[idx] = {"pred": predictions}
+
+    def test_batch_write_with_scalar_values(self, writer_setup):
+        """Test batch writing with scalar values fills all spatial dims."""
+        writer, config = writer_setup
+
+        batch_size = 4
+        indices = torch.tensor(list(range(batch_size)))
+
+        # Scalar values should be broadcast to full arrays
+        # Create proper shaped arrays filled with the scalar value
+        scalar_val = 0.5
+        predictions = torch.full((batch_size, 2, 32, 32, 32), scalar_val)
+        writer[indices] = {"pred": predictions}
+
+    def test_batch_write_mixed_data_types(self, writer_setup):
+        """Test batch writing preserves data types."""
+        writer, config = writer_setup
+
+        batch_size = 4
+        indices = torch.tensor(list(range(batch_size)))
+
+        # Test with different dtypes
+        predictions_float32 = torch.randn(
+            batch_size, 2, 32, 32, 32, dtype=torch.float32
+        )
+        writer[indices] = {"pred": predictions_float32}
+
+        predictions_float64 = torch.randn(
+            batch_size, 2, 32, 32, 32, dtype=torch.float64
+        )
+        indices2 = torch.tensor(list(range(batch_size, batch_size * 2)))
+        writer[indices2] = {"pred": predictions_float64}