Vision-based remote photoplethysmography (rPPG) enables non-contact heart rate measurement using consumer-grade cameras. However, the reliability assessment methods of rPPG predictions in out-of-distribution scenarios remains limited. To address this challenge, we propose Cali-rPPG, a unified uncertainty-aware framework with modular design that provides reliable reference-free uncertainty estimations. The framework comprises three modules: a rPPG module that generates heart rate estimations, an uncertainty module that predicts heart rate distributions and a calibration module that produces calibrated heart rate distributions whose uncertainty aligns with actual prediction errors. We comprehensively evaluate this framework across fifteen existing rPPG methods on five public databases, demonstrating its effectiveness. The proposed framework offers significant value for applications requiring reliable rPPG measurements in real-world settings. Code available at: https://github.com/stzzz99289/calirppg .