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\section{Conclusions}\label{sec:conclusions}
In this paper, we have established that the abstention mechanism is not merely a classification trick, but a fundamental and robust computer vision strategy whose capabilities are exceptionally beneficial for medical image segmentation. By introducing a universal framework equipped with informed regularization and dynamic auto-tuning, we have successfully generalized abstention to function with diverse loss paradigms, including the region-based Dice loss. This modularity allows for the creation of specialized noise-robust loss functions without requiring complex, computationally expensive changes to the underlying model architecture.

Our extensive validation on the CaDIS and DSAD datasets confirms that this approach yields consistent and significant improvements over standard baselines, particularly in high-noise situations where traditional models fail. The ability of the ADS model to produce clean, anatomically coherent masks even when trained on 25\% corrupted labels highlights the practical value of allowing models to selectively ignore unreliable supervision. 

While our results are promising, we acknowledge that our evaluation relied on synthetic noise injection, which serves as a controlled proxy for the complex, structured ambiguity often found in clinical annotations. Furthermore, the current framework relies on a pre-estimated noise rate hyperparameter. Future work will focus on validating this framework on datasets with naturally occurring inter-rater variability and developing adaptive, data-driven methods to estimate the noise rate dynamically. Ultimately, this work provides a scalable pathway toward building trustworthy diagnostic systems that can robustly learn from the imperfect data realities of the medical domain.