Survival analysis is a key area of statistics that focuses on modeling time-to-event data, with applications in many fields including clinical trials, engineering, and marketing. For example, in a clinical trial, researchers may aim to predict how long a cancer patient is likely to survive based on individual characteristics and treatments received. Two central goals are modeling the probability that an event will not occur before a given time and predicting the actual event time. These tasks are typically complicated by the fact that the data are censored--the exact event time may be unknown due to study limitations or participant withdrawal. While traditional methods, such as the Kaplan-Meier estimator and parametric models like the Cox proportional hazards model (Cox, 1972), are valued for their interpretability, they tend to struggle in high-dimensional settings or when their assumptions are violated. As a result, machine learning (ML) approaches are gaining popularity (Ishwaran et al., 2008; Katzman et al., 2018), despite the difficulty of obtaining uncertainty estimates and statistical guarantees. A promising approach to integrating ML models with rigorous statistical guarantees in survival analysis was recently introduced by Candès et al. (2023) and refined by Gui et al. (2024). Their conformal inference (Vovk et al., 2005; Lei & Wasserman, 2014) framework can use any survival