With the recent advancement of the use of supervised deep learning in applications in the field of computer vision, the industry and the academic community have been showing that one of the main difficulties for the success of these applications is the lack of datasets with a sufficient amount of annotated data. In this sense, there is a need to leverage large amounts of labeled data so that these intelligent models can solve problems relevant to their context to achieve the desired results. The use of techniques to generate annotated data more efficiently is being increasingly explored, together with techniques to support the generation of datasets that serve as inputs for the training of artificial intelligence models. This work aims to propose strategies to optimize annotation processes and generation of semantic segmentation datasets. Among the approaches used in this work, we highlight Interactive Segmentation and Active Learning. The first one tries to improve the data annotation process, making it more efficient and effective from the point of view of the annotator or specialist responsible for labeling the data using a semantic segmentation model that tries to imitate the annotations made by the annotator. The second consists of an approach that allows consolidating a deep learning model using an intelligent criterion, aiming at the selection of more informative unannotated data for training the model from an acquisition function that is based on the uncertainty estimation of the network to filter these data. To apply and validate the results of both techniques, the work incorporates them in a use case in mammography images for segmentation of anatomical structures.