Uncertainty in medical image segmentation is inherently non-uniform, with boundary regions exhibiting substantially higher ambiguity than interior areas. Conventional training treats all pixels equally, leading to unstable optimization during early epochs when predictions are unreliable. We argue that this instability hinders convergence toward Pareto-optimal solutions and propose a region-wise curriculum strategy that prioritizes learning from certain regions and gradually incorporates uncertain ones, reducing gradient variance. Methodologically, we introduce a Pareto-consistent loss that balances trade-offs between regional uncertainties by adaptively reshaping the loss landscape and constraining convergence dynamics between interior and boundary regions; this guides the model toward Pareto-approximate solutions. To address boundary ambiguity, we further develop a fuzzy labeling mechanism that maintains binary confidence in non-boundary areas while enabling smooth transitions near boundaries, stabilizing gradients, and expanding flat regions in the loss surface. Experiments on brain metastasis and non-metastatic tumor segmentation show consistent improvements across multiple configurations, with our method outperforming traditional crisp-set approaches in all tumor subregions.

翻译：医学图像分割中的不确定性本质上是不均匀的，边界区域表现出比内部区域显著更高的模糊性。传统训练方法对所有像素一视同仁，导致在预测不可靠的早期训练阶段出现不稳定的优化过程。我们认为这种不稳定性阻碍了模型向帕累托最优解的收敛，并提出一种区域渐进式课程学习策略，该策略优先从确定性区域学习，并逐步纳入不确定性区域，从而降低梯度方差。在方法上，我们提出一种帕累托一致性损失函数，通过自适应重塑损失曲面并约束内部区域与边界区域之间的收敛动态，来平衡区域不确定性之间的权衡；这引导模型逼近帕累托最优解。为处理边界模糊问题，我们进一步开发了一种模糊标注机制，该机制在非边界区域保持二元置信度的同时，允许边界附近实现平滑过渡，从而稳定梯度并扩展损失曲面中的平坦区域。在脑转移瘤与非转移性肿瘤分割任务上的实验表明，我们的方法在多种配置下均取得稳定提升，在所有肿瘤子区域的分割性能均优于传统的清晰集方法。