Survival analysis can be a useful tool for modeling the attrition of service members, particularly when it comes to forecasting future states of survival for those members. Government sponsors are often interested in predicting these attrition rates at future time points. IDA has developed a tool for this purpose: the Finite Interval Forecasting Engine (FIFE). FIFE is a forecasting tool that produces predictions with various modeling frameworks, including deep neural networks and gradient boosted trees. FIFE combines methods from both survival analysis and multivariate time series analysis to predict future states of survival, along with total counts of attrition, for service members at various future points in time. This paper discusses methods for quantifying uncertainty in these survival forecasts both for individual probabilities of exit and aggregated total exits. While FIFE currently uses advanced approaches for maximizing forecasting performance, through the use of Light GBM for gradient boosted trees and Keras for neural networks, there are currently little to no implemented methods for measuring uncertainty in these predictions.