MC Dropout Uncertainty Alignment Insufficient for Clinical Safety in Glioma Segmentation

A study on 126 BraTS21 patients finds that while MC Dropout achieves strong uncertainty-error alignment, it fails to detect critical calibration issues in enhancing tumour regions. The UNet-Res model shows near-zero entropy and high ECE in these clinically vital areas, with a low Dice score of 0.714, indicating severe miscalibration invisible to standard metrics like Dice and AUROC. These results highlight that uncertainty alignment alone is insufficient for clinical safety and that region-specific calibration must be evaluated alongside standard metrics.