Using anti-scatter grids in digital breast tomosynthesis (DBT) is challenging due to the relative motion between source and detector. Therefore, algorithmic scatter correction methods could be beneficial to compensate for the image quality loss due to scatter radiation. In this work, we present a deep learning model capable of predicting the scatter fraction (SF) image for simulations of realistically shaped homogeneous phantoms. The model was trained, validated, and tested on a dataset of 600 homogeneous phantoms representing the compressed breast with thicknesses ranging from 30 mm to 89 mm with randomly generated breast shapes. Monte Carlo simulations were performed for all cases and at different projection angles to obtain estimates of the DBT primary and scatter projections. The same procedure was performed for patient-based phantoms with realistic internal glandular and adipose texture to evaluate the generalizability of our model results. The median and interquartile range (IQR) of the mean relative difference (MRD) and mean absolute error (MAE) for the homogeneous phantoms between Monte Carlo SF ground truth and model predictions resulted in approximately 0.49% (IQR, 0.26-0.76%) and 2.06% (IQR, 1.83-2.26%), respectively, while the patient-based phantoms resulted in results with a MRD of -1.39% (IQR, -4.13-1.60%) and a MAE of 3.97% (IQR, 3.38-4.71%). This seems to indicate that the model trained on the homogeneous phantoms captures the average representation inside the homogeneous breast, with reasonable accuracy in breasts with texture variations. Therefore, the model has the potential to be used as an algorithmic scatter correction method in the future.