In disease followup, a biomarker may be measured longitudinally at follow up visits with a provider and is relevant for predicting patient prognosis and monitoring of disease progression. When a patient presents for a visit and the biomarker measured, the provider is tasked with scheduling the next visit for screening. Frequent measurement of the biomarker may provide little new information while possibly increasing patient discomfort or medical costs. Delayed measurement may miss opportunities to interact with the patient if they have already progressed. Thus, optimizing and personalizing the screening interval for the patient is key. We frame this tradeoff using quantiles of residual lifetime, where the screening time for the next biomarker assessment is subject to a preselected risk threshold or quantile of conditional survival for the progression event. We propose a two-step framework to model the longitudinal biomarker with subject-specific random effects and estimate quantiles of residual lifetiem using a plug-in of random effect estimates. Methods are applied to a study of patients following radical prostatectomy with screening of prostate specific antigen.