<ul><li>Survival analysis is crucial in various real-world applications like healthcare and risk assessment, but quantifying prediction uncertainty remains a challenge.</li><li>To address this issue, a novel framework called SurvUnc has been introduced for uncertainty quantification in survival models.</li><li>SurvUnc utilizes an anchor-based learning strategy integrating concordance knowledge to effectively estimate uncertainty.</li><li>Extensive experiments on benchmark datasets and survival models demonstrate the effectiveness of SurvUnc in enhancing model interpretability and reliability.</li></ul>

SurvUnc: A Meta-Model Based Uncertainty Quantification Framework for Survival Analysis

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