Uncertainty estimation is essential for the safe clinical deployment of medical image segmentation systems, enabling the identification of unreliable predictions and supporting human oversight. While prior work has largely focused on pixel-level uncertainty, landmark-based segmentation offers inherent topological guarantees yet remains underexplored from an uncertainty perspective. In this work, we study uncertainty estimation for anatomical landmark-based segmentation on chest X-rays. Inspired by hybrid neural network architectures that combine standard image convolutional encoders with graph-based generative decoders, and leveraging their variational latent space, we derive two complementary measures: (i) latent uncertainty, captured directly from the learned distribution parameters, and (ii) predictive uncertainty, obtained by generating multiple stochastic output predictions from latent samples. Through controlled corruption experiments we show that both uncertainty measures increase with perturbation severity, reflecting both global and local degradation. We demonstrate that these uncertainty signals can identify unreliable predictions by comparing with manual ground-truth, and support out-of-distribution detection on the CheXmask dataset. More importantly, we release CheXmask-U (huggingface.co/datasets/mcosarinsky/CheXmask-U), a large scale dataset of 657,566 chest X-ray landmark segmentations with per-node uncertainty estimates, enabling researchers to account for spatial variations in segmentation quality when using these anatomical masks. Our findings establish uncertainty estimation as a promising direction to enhance robustness and safe deployment of landmark-based anatomical segmentation methods in chest X-ray. A fully working interactive demo of the method is available at huggingface.co/spaces/matiasky/CheXmask-U and the source code at github.com/mcosarinsky/CheXmask-U.

翻译：不确定性估计对于医学图像分割系统的安全临床部署至关重要，它能够识别不可靠的预测结果并支持人工监督。尽管先前的研究主要集中在像素级不确定性上，但基于解剖标志点的分割方法具有固有的拓扑结构保证，而从不确定性角度对其进行的探索仍显不足。本研究针对胸部X射线图像中基于解剖标志点的分割方法，开展不确定性估计研究。受结合标准图像卷积编码器与基于图的生成解码器的混合神经网络架构启发，并利用其变分潜在空间，我们推导出两种互补的度量指标：（i）潜在不确定性，直接从学习到的分布参数中捕获；（ii）预测不确定性，通过从潜在样本生成多个随机输出预测获得。通过受控的噪声干扰实验，我们证明这两种不确定性度量均随干扰强度的增加而增加，反映了全局与局部的图像退化程度。我们通过与人工标注真值对比，验证了这些不确定性信号能够识别不可靠的预测结果，并在CheXmask数据集上支持分布外检测。更重要的是，我们发布了CheXmask-U数据集（huggingface.co/datasets/mcosarinsky/CheXmask-U），这是一个包含657,566个胸部X射线解剖标志点分割结果的大规模数据集，其中每个节点均附有不确定性估计值，使研究人员在使用这些解剖掩模时能够考虑分割质量的空间差异性。我们的研究结果表明，不确定性估计是提升基于标志点的胸部X射线解剖分割方法鲁棒性与安全部署前景的重要方向。该方法的完整交互式演示可在huggingface.co/spaces/matiasky/CheXmask-U访问，源代码发布于github.com/mcosarinsky/CheXmask-U。