Strong uncertainty metrics alone don't guarantee clinical safety: medical AI models need region-specific calibration checks, not just overall accuracy and uncertainty scores, before deployment.
This paper investigates whether Monte Carlo Dropout can reliably detect segmentation errors in brain tumor MRI scans. While the method showed strong overall uncertainty-error alignment, it failed to catch critical miscalibration in clinically important tumor regions—a failure invisible to standard metrics.