diff --git a/test.py b/test.py
index ab914c8..9ba09cc 100644
--- a/test.py
+++ b/test.py
@@ -8,6 +8,7 @@
 """
 
 import argparse
+import csv
 import numpy as np
 import nibabel as nib
 import os
@@ -188,6 +189,11 @@ def main():
     else:
         # Fetch existing models in the current directory. The models are assumed to be named "best_metric_model*.pth"
         path_models = [f for f in os.listdir('.') if os.path.isfile(f) and f.startswith('best_metric_model')]
+
+    # Check that if -u is specified, there is more than one model state file
+    if args.uncertainty and len(path_models) == 1:
+        raise ValueError("The -u flag is only valid if there is more than one model state file.")
+
     # Load the trained 2D U-Net models
     models = []
     for path_model in path_models:
@@ -218,7 +224,9 @@ def main():
     with torch.no_grad():
         segmented_slices = []
         segmented_slices_std = []
-        for data in tqdm(dataloader, desc=f"Segment image", unit="image"):
+        prediction_std_list = []
+
+        for index, data in enumerate(tqdm(dataloader, desc="Segment image", unit="image")):
             image = data["image"].to(device)
             # TODO: parametrize values below
             roi_size = (192, 192)
@@ -248,6 +256,11 @@ def main():
                 #  segmented_slice_ensemble = np.mean(segmented_slice_ensemble_all, axis=0)
                 if args.uncertainty:
                     segmented_slice_ensemble_std = np.std(segmented_slice_ensemble_all, axis=0)
+                    # Calculate the average across non-null values
+                    non_null_values = np.where(np.isnan(segmented_slice_ensemble_all), 0, segmented_slice_ensemble_all)
+                    prediction_std = np.mean(non_null_values)
+                    # print(f"Slice #{index}, Prediction STD: {prediction_std}")
+                    prediction_std_list.append([index, prediction_std])
             # Add the segmented slice to the list of segmented slices
             segmented_slices.append(segmented_slice_ensemble_all_aggregated)
             if args.uncertainty:
@@ -276,6 +289,13 @@ def main():
 
         print(f"Done! Output file: {fname_out}")
 
+        # Write the data to the CSV file
+        output_file = 'prediction_std.csv'
+        with open(output_file, 'w', newline='') as csvfile:
+            writer = csv.writer(csvfile)
+            writer.writerow(['Index', 'Prediction STD'])  # Write the header
+            writer.writerows(prediction_std_list)  # Write the data rows
+
 
 if __name__ == "__main__":
     main()