Medical image segmentation is crucial for clinical applications, but it is frequently disrupted by noisy annotations and ambiguous anatomical boundaries, limiting its application in real-world scenarios. Existing methods often directly adapt noisy label learning techniques designed for instance classification, overlooking the pixel-wise heterogeneity in medical segmentation with its spatially and anatomically varying difficulties. Consequently, global assumptions or simple confidence metrics fail to address these local variations, leaving boundary ambiguities unresolved. To address this issue, we propose MetaDCSeg, a robust framework that dynamically learns optimal pixel-wise weights to suppress the influence of noisy labels while preserving reliable annotations. By explicitly modeling boundary uncertainty through a Dynamic Center Distance (DCD) mechanism, our approach utilizes weighted feature distances for foreground, background, and boundary centers, directing the model's attention toward hard-to-segment pixels near ambiguous boundaries. This strategy enables more precise handling of structural boundaries, which are often overlooked by existing methods, and significantly enhances segmentation performance. Extensive experiments across four benchmark datasets with varying noise levels demonstrate that MetaDCSeg outperforms existing state-of-the-art methods.

翻译：医学图像分割在临床应用中至关重要，但常受噪声标注和模糊解剖边界干扰，限制了其在真实场景中的应用。现有方法通常直接套用为实例分类设计的噪声标签学习技术，忽视了医学分割中像素级的异质性及其在空间和解剖结构上变化的分割难度。因此，全局假设或简单的置信度度量无法应对这些局部变化，导致边界模糊问题未能解决。针对此问题，我们提出MetaDCSeg——一个通过动态学习最优像素级权重来抑制噪声标签影响、同时保留可靠标注的鲁棒框架。通过动态中心距离机制显式建模边界不确定性，本方法利用加权特征距离计算前景、背景及边界中心，将模型注意力引导至模糊边界附近难以分割的像素。该策略能更精确地处理常被现有方法忽略的结构边界，显著提升分割性能。在四个不同噪声水平的基准数据集上的大量实验表明，MetaDCSeg优于现有最先进方法。