Automated skill evaluation of a trainee is key to the utility of the surgical training system. The focus of this paper is to develop an automated tool for the assessment of trainees for any suturing task. The real-life training datasets for the micro-suturing task are often small, with long-tailed distribution, making it difficult to develop machine-learning-based tools for automated assessment. Further, micro-suturing is often performed at various magnifications and suture sizes, which makes the automated assessment more challenging compared to macro-suturing. Hence, currently, assessment is done manually by an expert using the final outcome image. In this paper, we propose a multi-task learning-based convolutional-neural-network regression model to score the effectualness of the micro-suturing task from the final outcome image. We propose a novel equivalent of the logit-adjustment (used in classification) applicable to regression formulation which effectively handles the problems associated with the long-tail distribution of the data. Additionally, we contribute the largest open-access dataset for suturing images and the first dataset pertaining to the micro-suturing task. We also demonstrate that the performance of the proposed algorithm surpasses the performance of human experts and also other state-of-the-art (SOTA) algorithms.