UNet-Res

MC Dropout uncertainty estimation masks sub-region calibration failures in brain tumour segmentation

A preprint from arXiv evaluates Monte Carlo Dropout for voxel-level uncertainty estimation in glioma segmentation on 126 BraTS21 patients, comparing a pretrained SegResNet and a locally trained UNet-Res. While global uncertainty-error alignment is strong (AUROC ~0.97), the study finds that UNet-Res exhibits near-zero entropy and an ECE of 0.915 on the enhancing tumour sub-region despite a Dice of only 0.714, a severe miscalibration invisible to standard Dice and AUROC metrics. The paper argues that sub-region-specific calibration assessment is necessary for clinical safety and cannot be replaced by aggregate metrics alone.