An Empirical Study on Variance-based MC Dropout Uncertainty-Error Correlation in 2D Brain Tumor Segmentation

arXiv:2510.15541v2 Announce Type: replace Abstract: Accurate brain tumor segmentation from MRI is vital for diagnosis and treatment planning. Although Monte Carlo (MC) Dropout is widely used to estimate model uncertainty, the effectiveness of variance-based uncertainty - computed as pixel-wise variance across stochastic forward passes - in identifying segmentation errors, particularly near tumor boundaries, remains insufficiently studied. This study empirically examines the relationship between variance-based MC Dropout uncertainty and segmentation error in 2D brain tumor MRI segmentation usin

Source: arXiv cs.LG — read the full report at the original publisher.

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