Diabetic retinopathy (DR) is one of the serious complications of diabetes, and if left untreated, it can severely damage vision. Therefore, early detection and accurate grading are crucial for the prevention and treatment of DR. Deep learning models can assist in the diagnosis and grading of DR, thereby reducing the screening burden. However, existing methods are often limited by inaccurate localization of abnormalities, large variations in lesion sizes, the susceptibility to losing tiny lesions, and the high cost of pixel-level annotation. To address these issues, we propose a saliency-guided multi-task attention network (SMANet) that jointly optimizes saliency segmentation and DR grading tasks. Extracting multi-level features through a shared encoder and designing multi-scale spatial attention block (MSAB) to enhance multi-scale context-awareness and global dependency modeling. Designing pixel-level saliency map segmentation task to encourage model to focus on lesion regions while preserving fine-grained features and mitigating pixel-level annotation costs. Further, the ordered regularization module (ORM) is introduced in the grading task to improve the grading accuracy by exploiting the ordering of disease labels. Experiments on the DDR and APTOS2019 datasets show that SMANet achieves grading accuracies of 78.40% and 85.46%, and Kappa scores of 79.73% and 91.49%, respectively, which are superior to existing methods.