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Retinal age has recently emerged as a reliable ageing biomarker for assessing risks of ageing-related diseases. Several studies propose to train deep learning models to estimate retinal age from fundus images. However, the limitation of these studies lies in 1) both of them only train models on snapshot images from single cohorts; 2) they ignore label ambiguity and individual variance in the modeling part. In this study, we propose a progressive label distribution learning (LDL) method with temporal fundus images to improve the retinal age estimation on snapshot fundus images from multiple cohorts. First, we design a two-stage LDL regression head to estimate adaptive age distribution for individual images. Then, we eliminate cohort variance by introducing ordinal constraints to align image features from different data sources. Finally, we add a temporal branch to model sequential fundus images and use the captured …