Screening mammogram is a standard process to measure breast cancer risk among 45+ year old women. Quantifying breast arterial calcification (BAC) from screening mammograms is a non-invasive and cost-efficient approach to assess the future risk of cardiovascular diseases (CVD) among women, such as heart attack and stroke. However, segmentation of breast arterial calcification is an involved task and poses several technical challenges such as extremely small BAC finding - low breast arteries to breast area ratio in the mammogram images; tissue features such as breast folds and heterogeneous density look very similar to BAC. In this work, we aim to address the shortcomings of existing SOTA methods, e.g., SCUNet, and analyze the comparative performance. We propose a multi-task learning approach for BAC segmentation by adding an auxiliary task of patch position prediction based on prior knowledge about anatomy. The proposed method achieves state-of-the-art performance compared to the baselines. To demonstrate the utility, we also validate our method on external data and provide survival analysis for CVD based on the BAC score.