Deep learning-powered iterative combinatorial auctions (DL-ICA) are auctions that utilize machine learning techniques. Unlike traditional auctions, bidders in DL-ICA do not need to report the valuations for all bundles upfront. Instead, they report their value for certain bundles iteratively, and the allocation of the items is determined by solving a winner determination problem. During this process, the bidder profiles are modeled with neural networks. However, DL-ICA may not always achieve the optimal winner allocation due to the relatively low number of reported bundles, resulting in reduced economic efficiency. This paper proposes an algorithm that uses active learning for initial sampling strategies to improve the resulting economic efficiency (social welfare). The proposed algorithm outperforms previous studies in real-world combinatorial auction models across various domains while using fewer samples on average.