Background: Automatically generated ECG segmentations enable large-scale analysis, but their reliability varies between records. Estimating segmentation quality is therefore important for deciding which outputs can be trusted.
Method: We propose RTrust, a self-supervised reconstruction model trained on reference segmentation masks. Reconstruction loss provides a per-record trust reward, evaluated both as a reference-free confidence estimate and as a weighting or selection criterion for knowledge distillation.
Results: RTrust separates reference from degraded masks with AUROC from 0.566 to 0.984 depending on corruption severity. On model outputs, the in-domain reward correlates with per-record F1 at ρ = 0.225–0.515. Retaining the highest-reward 25% improves pooled macro F1 from 0.754 to 0.796 on QTDB and from 0.807 to 0.866 on LUDB. In contrast, reward-guided weighting or data selection changes held-out F1 by less than 0.006.
Conclusion: RTrust provides a useful reference-free estimate of ECG segmentation reliability, but does not yield a measurable benefit when used to guide distillation.