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Visual Question Answering (VQA) is a computer vision task in which a system produces an accurate answer to a given image and a question that is relevant to the image. Medical VQA can be considered as a sub field of general VQA, which focuses on images and questions in the medical domain. The VQA model's most crucial task is to learn the question-image joint representation to reflect the information related to the correct answer. Medical VQA remains a difficult task due to the ineffectiveness of question-image embeddings, despite recent research on general VQA models finding significant progress. To address this problem, we propose a new method for training VQA models that utilize adversarial learning to improve the question-image embedding and illustrate how those embedding can be used as the ideal embedding for answer inference. For adversarial learning, we use two embedding generators (question-image embedding and a question-answer embedding generator) and a discriminator to differentiate the two embeddings. The question-answer embedding is used as the ideal embedding and the question-image embedding is improved in reference to that. The experiment results indicate that pre-training the question-image embedding generation module using adversarial learning improves overall performance, implying the effectiveness of the proposed method.