Uncertainty quantification (UQ) is critical for safety-critical domains like healthcare, yet it is rarely evaluated under realistic out-of-distribution (OOD) conditions. Here, we assessed predictive performance and uncertainty reliability for deep learning-based blood pressure (BP) estimation from photoplethysmography (PPG) signals under both in-distribution (ID) and OOD settings. Using an XResNet1D-50 trained on PulseDB and tested on four external datasets, we compared deep ensembles (DE) and Monte Carlo dropout (MCD) with Gaussian negative log-likelihood (GNLL) and mean squared error (MSE) losses, optionally followed by post-hoc recalibration via conformal prediction (CP), temperature scaling (TS), and isotonic regression (IR). The key findings of our study are as follows: (1) DE provides stronger predictive robustness under domain shift than MCD, an advantage that becomes clear primarily under external shift. (2) Recalibrated GNLL-based methods yield the best uncertainty calibration (e.g., GNLL+DE+CP for systolic blood pressure (SBP), GNLL+DE+TS for diastolic blood pressure (DBP)), while MSE-based uncertainty requires recalibration to become practically useful. (3) Across settings, CP and TS offer the most consistent gains, with IR remaining competitive in several cases. Overall, our results identify DE-based methods as most robust for predictive performance under domain shift, GNLL as strongest for native UQ, and recalibration as essential for making MSE-based uncertainty practical. These findings highlight the need to jointly assess predictive accuracy and calibration on external data for trustworthy cuffless BP estimation

翻译：不确定性量化（UQ）在医疗等安全关键领域至关重要，但当前鲜有研究在真实分布外（OOD）条件下评估其性能。本文针对基于深度学习的光电容积脉搏波（PPG）血压（BP）估计方法，在分布内（ID）和OOD场景下系统评估了预测性能与不确定性可靠性。我们采用在PulseDB上训练、在四个外部数据集上测试的XResNet1D-50模型，比较了深度集成（DE）和蒙特卡洛丢弃法（MCD）分别联合高斯负对数似然（GNLL）与均方误差（MSE）损失函数的性能，并可选地通过保形预测（CP）、温度缩放（TS）和等渗回归（IR）进行事后重校准。主要研究发现如下：（1）在域迁移下，DE比MCD具有更强的预测鲁棒性，该优势在外部迁移场景中尤为显著；（2）基于GNLL且经重校准的方法可获得最优不确定性校准效果（例如收缩压（SBP）采用GNLL+DE+CP，舒张压（DBP）采用GNLL+DE+TS），而基于MSE的不确定性需经重校准才能具备实用价值；（3）在各场景下，CP和TS能带来最稳定的性能提升，IR在多数情况下也保持竞争力。总体而言，我们的结果表明：DE方法在域迁移下具有最优预测鲁棒性，GNLL在原生UQ方面表现最强，而重校准是使MSE不确定性具备实用性的关键。这些发现强调了在外部数据上联合评估预测精度与校准性能对于可信无袖带血压估计的重要性。