Suffix prediction of business processes forecasts the remaining sequence of events until process completion. Current approaches focus on predicting the most likely suffix, representing a single scenario. However, when the future course of a process is highly uncertain and variable, a single scenario may have limited predictive value. To address this limitation, we propose probabilistic suffix prediction, a novel approach that returns a set of sampled suffixes. The method is based on an uncertainty-aware encoder-decoder LSTM combined with a Monte Carlo suffix sampling algorithm. We capture epistemic uncertainty via MC dropout and aleatoric uncertainty as learned loss attenuation. Comparisons with two other uncertainty-aware PPM approaches across four datasets demonstrate that our probabilistic suffix prediction approach achieves reasonable predictive performance while it allows estimating prediction intervals for multiple objectives within a single model.