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Recently, convolutional neural networks (CNNs) have proven successful in automating the detection of defective photovoltaic (PV) cells within PV modules. Existing studies have built a CNN based on fully supervised learning, which requires a training dataset consisting of PV cell images annotated according to whether the individual cells are defective. However, manually annotating all the PV cells is labor-intensive and time-consuming, leading to substantial annotation costs. In this study, we propose a weakly supervised learning method to build a CNN for cell-level defect detection in a cost-efficient manner. Our method uses a training dataset solely with module-level annotations indicating whether each PV module contains defective cells, thereby substantially reducing the required annotation costs. The CNN is trained in a weakly supervised manner such that all cells in a normal module are classified as normal and at least one cell in a defective module is classified as defective. The CNN can then be used to detect cell-level defects in new PV modules. The effectiveness of the proposed method is validated through experiments using real-world data provided by a PV module manufacturer.