Reliable semantic segmentation is essential for clinical decision making, yet deep models rarely provide explicit statistical guarantees on their errors. We introduce a simple post-hoc framework that constructs confidence masks with distribution-free, image-level control of false-positive predictions. Given any pretrained segmentation model, we define a nested family of shrunken masks obtained either by increasing the score threshold or by applying morphological erosion. A labeled calibration set is used to select a single shrink parameter via conformal prediction, ensuring that, for new images that are exchangeable with the calibration data, the proportion of false positives retained in the confidence mask stays below a user-specified tolerance with high probability. The method is model-agnostic, requires no retraining, and provides finite-sample guarantees regardless of the underlying predictor. Experiments on a polyp-segmentation benchmark demonstrate target-level empirical validity. Our framework enables practical, risk-aware segmentation in settings where over-segmentation can have clinical consequences. Code at https://github.com/deel-ai-papers/conseco.

翻译：可靠的语义分割对于临床决策至关重要，然而深度模型很少对其误差提供明确的统计保证。我们引入了一种简单的后处理框架，该框架构建置信掩码，实现对假阳性预测的分布无关、图像级控制。给定任何预训练的分割模型，我们定义了一个嵌套的收缩掩码族，这些掩码通过提高分数阈值或应用形态学腐蚀获得。使用带标签的校准集通过共形预测选择单个收缩参数，确保对于与校准数据可交换的新图像，置信掩码中保留的假阳性比例以高概率保持在用户指定的容差以下。该方法与模型无关，无需重新训练，并且无论底层预测器如何，都能提供有限样本保证。在息肉分割基准上的实验证明了目标级的经验有效性。我们的框架在过度分割可能产生临床后果的场景中实现了实用、风险感知的分割。代码位于 https://github.com/deel-ai-papers/conseco。