Polygenic Scores (PGSs) summarize an individual's genetic propensity for a given trait. Bayesian methods, which improve the prediction accuracy of PGSs, are not well-calibrated for binary disorder traits in ascertained samples. This is a problem because well-calibrated PGSs are needed for future clinical implementation. We introduce the Bayesian polygenic score Probability Conversion (BPC) approach, which computes an individual's predicted disorder probability using genome-wide association study summary statistics, an existing Bayesian PGS method (e.g. PRScs, SBayesR), the individual's genotype data, and a prior disorder probability (which can be specified flexibly, based for example on literature, small reference samples, or prior elicitation). The BPC approach is practical in its application as it does not require a tuning sample with both genotype and phenotype data. Here, we show in simulated and empirical data of nine disorder traits that BPC yields well-calibrated results that are consistently better than the results of another recently published approach.