Hallucinations undermine the reliability of Large Language Models (LLMs) in real-world settings. Current contrastive decoding methods mitigate hallucinations by generating multiple responses based on an amateur model and an expert model or using the logit distribution across all layers, leading to considerable redundancy and computational inefficiency during inference. To address these limitations, we propose SHICD (Self-Hallucination-Inducing Contrastive Decoding), a novel training-free decoding framework that requires a single response from an individual model and leverages information from a subset of all layers, without any external intervention on activations. Specifically, SHICD first identifies the inducing layer of the LLM when an initial semantic representation emerges in the model. The hallucination direction is then extracted to induce hallucinations by itself while preserving the initial semantic information encoded before the inducing layer. Finally, we employ an adaptive contrastive decoding mechanism to determine when to apply contrastive decoding during generation. Extensive experiments across multiple LLMs and benchmark datasets demonstrate that SHICD significantly improves factual accuracy over baseline methods while achieving lower inference latency.