Effectiveness of MC Dropout Uncertainty for Localizing Segmentation Errors at Tumor Boundaries

Determine whether Monte Carlo Dropout–based uncertainty estimates in 2D brain tumor MRI segmentation reliably identify segmentation errors, particularly along tumor boundaries, by quantifying the strength of association between per-pixel uncertainty values and misclassification errors.

Background

Monte Carlo (MC) Dropout is a widely used approach to approximate Bayesian uncertainty in deep learning models, including for medical image segmentation tasks. In brain tumor segmentation, accurately identifying error-prone regions at tumor boundaries is clinically important because boundary inaccuracies can affect downstream clinical decisions.

The paper motivates its empirical study by noting that the practical effectiveness of MC Dropout for highlighting segmentation errors—especially at boundaries—has not been clarified. The authors then present an analysis for 2D brain tumor MRI segmentation using a U-Net, showing weak global correlations and negligible boundary correlations between uncertainty and error, suggesting limited utility of MC Dropout for boundary error localization in this setting.

References

Although Monte Carlo (MC) Dropout is widely used to estimate model uncertainty, its effectiveness in identifying segmentation errors—especially near tumor boundaries—remains unclear.

— An Empirical Study on MC Dropout--Based Uncertainty--Error Correlation in 2D Brain Tumor Segmentation  (2510.15541 - B, 17 Oct 2025) in Abstract

The uncertainty signals were descriptive and were not quantitatively validated against manual correction effort or dosimetric impact. Multi-institutional validation and prospective evaluation of uncertainty-guided review are needed before clinical deployment.

— Parameter-Efficient pretrained-CT-to-MRI Transfer for Rectal Cancer Segmentation: Performance-Calibration Trade-offs  (2608.27178 - Rangnekar et al., 27 Aug 2026) in Discussion and conclusions, Section 4

Can predictive uncertainty identify unsupported reconstructions?

— Virtual iEEG from Scalp EEG: Charting the Landscape of Source Imaging, Intracranial Inference and Reconstruction  (2608.26998 - He et al., 27 Aug 2026) in Section 7, “Outstanding questions and research priorities”