Survival prediction from medical data is often constrained by scarce labels, limiting the effectiveness of fully supervised models. In addition, most existing approaches produce deterministic risk scores without conveying reliability, which hinders interpretability and clinical trustworthiness. To address these challenges, we introduce T-SURE, a transductive survival ranking and risk-stratification framework that learns jointly from labeled and unlabeled patients to reduce dependence on large annotated cohorts. It also estimates a rejection score that identifies high-uncertainty cases, enabling selective abstention when confidence is low. T-SURE generates a single risk score that enables (1) patient ranking based on survival risk, (2) automatic assignment to risk groups, and (3) optional rejection of uncertain predictions. We extensively evaluated the model on pan-cancer datasets from The Cancer Genome Atlas (TCGA), using gene expression profiles, whole slide images, pathology reports, and clinical information. The model outperformed existing approaches in both ranking and risk stratification, especially in the limited labeled data regimen. It also showed consistent improvements in performance as uncertain samples were rejected, while maintaining statistically significant stratification across datasets. T-SURE integrates as a reliable component within computational pathology pipelines by guiding risk-specific therapeutic and monitoring decisions and flagging ambiguous or rare cases via a high rejection score for further investigation. To support reproducibility, the full implementation of T-SURE is publicly available at: (Anonymized).
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