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In real-time speech enhancement models based on the short-time Fourier transform (STFT), algorithmic latency induced by the STFT window size can induce perceptible delays, leading to reduced immersion in real-time applications. This study proposes an efficient real-time enhancement model based on dense frequency-time attentive network (DeFT-AN). The vanilla DeFT-AN consists of cascaded dense blocks and time-frequency transformers, which allow for a smooth transition between time frames through a temporal attention mechanism. To inherit this advantage and reduce algorithmic latency, we develop the lightweight and causal version of DeFT-AN with dual-window size processing that utilizes synthesis windows shorter than analysis windows. The benefit of DeFT-AN in identifying temporal context enables the use of non-overlapping synthesis windows, and experimental results show that the model can achieve the highest performance with the lowest algorithmic latency among STFT-based models.