The probability of agreement (PoA) has been used as an effective strategy for quantifying the similarity between the reliability of two populations. In contrast to the p-value approach associated with hypothesis testing, the PoA provides a more realistic assessment of similarity by accounting for a practically important difference. This talk discusses the adaptation of the PoA to the comparison of population reliabilities as estimated by Kaplan-Meier curves, when lifetime data is right censored. Three methods for quantifying uncertainty in the PoA estimate are explored: the first approach provides a convenient assessment based on large sample approximations, and the second two approaches offer nonparametric bootstrap-based alternatives. One of these relies on the traditional bootstrap while the other is based on the fractional random-weight bootstrap, which is necessitated by high censoring rates. All methods are illustrated with examples for which comparing the reliability curves of related populations is of interest.