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, its effectiveness in identifying segmentation errors -- especially near tumor boundaries -- remains unclear. This study empirically examines the relationship between MC Dropout--based uncertainty and segmentation error in 2D brain tumor MRI segmentation using a U-Net trained under four augmentation settings: none, horizontal flip, rotation, and scaling. Uncertainty was computed from 50 stochastic forward passes and correlated with pixel-wise errors using Pearson and Spearman coefficients. Results show weak global correlations ($r \approx 0.30$--$0.38$) and negligible boundary correlations ($|r| < 0.05$). Although differences across augmentations were statistically significant ($p < 0.001$), they lacked practical relevance. These findings suggest that MC Dropout uncertainty provides limited cues for boundary error localization, underscoring the need for alternative or hybrid uncertainty estimation methods in medical image segmentation.

翻译：从MRI中精确分割脑肿瘤对于诊断和治疗规划至关重要。尽管蒙特卡洛（MC）Dropout被广泛用于估计模型不确定性，但其在识别分割误差——尤其是肿瘤边界附近误差——方面的有效性仍不明确。本研究使用在四种数据增强设置（无增强、水平翻转、旋转和缩放）下训练的U-Net，对二维脑肿瘤MRI分割中基于MC Dropout的不确定性与分割误差之间的关系进行了实证检验。通过50次随机前向传播计算不确定性，并使用皮尔逊和斯皮尔曼系数与像素级误差进行相关性分析。结果显示全局相关性较弱（$r \approx 0.30$--$0.38$），边界相关性可忽略不计（$|r| < 0.05$）。尽管不同增强方式间的差异具有统计显著性（$p < 0.001$），但缺乏实际意义。这些发现表明，MC Dropout不确定性为边界误差定位提供的线索有限，凸显了在医学图像分割中需要采用替代或混合不确定性估计方法。