Discrimination and calibration represent two important properties of survival analysis, with the former assessing the model’s ability to accurately rank subjects and the latter evaluating the alignment of predicted outcomes with actual events. With their distinct nature, it is hard for survival models to simultaneously optimize both of them. Previous results often found improving calibration tends to diminish discrimination performance. This paper introduces a novel approach utilizing conformal regression that can improve model’s calibration without degrading discrimination. We provide theoretical guarantees for the above claim, and rigorously validate and demonstrate the efficiency of our approach across 11 real-world datasets, showcasing its practical applicability and robustness in diverse scenarios.