<p>Accurate prediction of clinical outcomes and a reliable assessment of the uncertainty around those predictions are critical for informed decision-making in medicine. Yet, as black-box machine learning algorithms are increasingly deployed for complex biomedical prediction tasks, their lack of robust uncertainty quantification, especially in small samples, rare diseases, and underrepresented groups, has led to overconfident predictions and adverse outcomes. Conformal quantile regression (CQR) offers a distribution-free, model-agnostic framework that generates finite-sample valid prediction intervals, ensuring reliable coverage guarantees regardless of the underlying ML algorithm. While CQR has been applied in various prediction tasks, it has not been explored in the context of predicting estimated glomerular filtration rate (eGFR) using high-dimensional image-derived regressors, such as features extracted from kidney biopsy images. In this study, we investigate the utility of CQR for predicting eGFR, a key biomarker of kidney function, by integrating demographic, clinical, and image data. Through simulation studies, we demonstrate that CQR maintains reliable coverage even when predictions are constructed from generated regressors subject to additive measurement error, although larger sample sizes are necessary when regressors are functionally misspecified. Finally, we apply CQR to real-world glomerular disease kidney biopsy image features and show that it produces well-calibrated prediction intervals for future eGFR measurements, addressing a critical gap in quantifying predictive uncertainty in complex biomedical models.</p>